Identification of prostate cancer bone metastasis related genes and potential therapy targets by bioinformatics and in vitro experiments

Abstract The aetiology of bone metastasis in prostate cancer (PCa) remains unclear. This study aims to identify hub genes involved in this process. We utilized machine learning, GO, KEGG, GSEA, Single‐cell analysis, ROC methods to identify hub genes for bone metastasis in PCa using the TCGA and GEO databases. Potential drugs targeting these genes were identified. We validated these results using 16 specimens from patients with PCa and analysed the relationship between the hub genes and clinical features. The impact of APOC1 on PCa was assessed through in vitro experiments. Seven hub genes with AUC values of 0.727–0.926 were identified. APOC1, CFH, NUSAP1 and LGALS1 were highly expressed in bone metastasis tissues, while NR4A2, ADRB2 and ZNF331 exhibited an opposite trend. Immunohistochemistry further confirmed these results. The oxidative phosphorylation pathway was significantly enriched by the identified genes. Aflatoxin B1, benzo(a)pyrene, cyclosporine were identified as potential drugs. APOC1 expression was correlated with clinical features of PCa metastasis. Silencing APOC1 significantly inhibited PCa cell proliferation, clonality, and migration in vitro. This study identified 7 hub genes that potentially facilitate bone metastasis in PCa through mitochondrial metabolic reprogramming. APOC1 emerged as a promising therapeutic target and prognostic marker for PCa with bone metastasis.


| INTRODUC TI ON
Prostate cancer (PCa) has emerged as the most prevalent malignancy in males and the second leading cause of cancer-related deaths among men. 1 Although localized PCa is often curable, the number of patients initially diagnosed at a metastatic stage has increased significantly in the last decade. 2Metastasis, particularly in the bones, represents a major cause of mortality in patients with PCa, 3 affecting approximately 70%-90% of those with advanced disease.[6] Patients with bone metastases are at increased risk of fractures, severe bone pain, and paraplegia, which greatly diminish the quality of life and worsen prognosis. 7,8evious studies have demonstrated that the process of bone metastasis in Pca is a complex and sequential cascade involving various cellular interactions within the tumour microenvironment. 8,9Within this intricate process lies the potential influence of certain small RNAs on gene expression modulation and disease progression.1][12] Given the limited therapeutic options for PCa with bone metastasis, understanding the underlying molecular mechanisms holds immense clinical significance for improving patient outcomes.
[15] Machine learning (ML) draws on techniques from statistics, computer science, and artificial intelligence (AI) to provide new solutions for uncovering complex relationships and patterns that traditional statistical methods may not be able to elucidate, thereby helping in the development of more efficient algorithms. 16[19] In this study, we employed a series of bioinformatics methods, including ML, differential expression, enrichment, co-expression network, immune infiltration, and intercellular communication analyses to identify hub genes involved in PCa with bone metastasis.
These genes were subsequently validated using pathological tissue sections and clinical data from patients with PCa.Finally, the role of APOC1 in PCa cells was confirmed through in vitro experiments.
The flowchart of this study is shown in Figure 1.

| Data collection
TCGA-PRAD was downloaded from The Cancer Genome Atlas (TCGA) database.After eliminating duplicate patient samples, 532 samples (51 normal and 481 prostate adenocarcinoma (PRAD) samples) were obtained.

| Differentially expressed gene analysis
Differentially expressed genes 1 (DEGs1) between PRAD and normal groups in the TCGA-PRAD dataset were screened by DESeq2 package. 20The limma package 21 was used to identify candidate differentially expressed genes 2 (DEGs2) between

PCa and PCa with bone metastasis in GSE77930 and GSE32269
datasets.The screening criteria were both |log2FoldChange (FC)| >0.5 and adjusted (adj.)p < 0.05.Findmarker function was used to identify genes specifically related to PCa with bone metastasis from the GSE143791 dataset.Genes differentially expressed 3 (DEGs3) between tumour and non-tumour cells in bone metastases were assessed using a threshold of average log FC = 0.5 and adj.p < 0.05.

| Identification and function analyses of DEGs
After that, GO and KEGG enrichment analyses were performed to identify the functions and pathways affected by DEGs 2, DEGs 3 using the ClusterProfiler package. 22

K E Y W O R D S
bone metastasis, cell communication, hub genes, machine learning, prostate cancer, single cell analysis were used to identify signature genes in the GSE77930 dataset.The common signature genes derived from both algorithms were identified as hub genes.The diagnostic efficacy of these genes in GSE77930 and GSE32269 was assessed using Receiver Operating Characteristic (ROC) curves generated with the pROC package. 23Expression trends of hub genes in these datasets were visualized using the ggplot2 package. 24

| Gene set enrichment analysis (GSEA) and co-expression network of hub genes
To explore corresponding pathways and potential biological mechanisms, the clusterProfiler package 26 and org.Hs.eg.db packages 27 were used for enrichment analysis of hub genes.According to correlations between hub genes and the expression levels of each gene in the GSE77930 dataset, all genes were ranked.GSEA was conducted with the criteria of |NES| >1, NOM p < 0.05 and q < 0.25.
Furthermore, a co-expression network of hub genes was constructed by GeneMANIA database.

| Immune infiltration analysis and drug prediction
Differences in immune cells infiltration between PCa and PCa with bone metastasis were compared by Wilcoxon test.Spearman correlations were used to analyse the relationships between differential immune cells and between differential immune cells and hub genes.
The correlation diagram was created using the ggcorrplot package. 28ditionally, the Comparative Toxicogenomics Database (CTD) was used to identify drugs potentially targeting hub genes for the treating PCa with bone metastasis.Key drugs were identified by intersecting the predictions for all hub genes.The visualization of the drugs-hub genes network was accomplished using the Cytoscape package. 29

| Single cell analysis
The single-cell data from the GSE143791 dataset was filtered by seurat package 30  and Projection (UMAP) were used for visualizing high-dimensional data.Cell type were annotated based on the annotation file of the raw dataset, and the distribution of each cell type was compared between PCa with bone metastasis and normal samples using a t-test.
The expression of hub genes in annotated cells was visualized.
To understand cell interaction differences between PCa with bone metastasis and PCa, cellular communication analysis was performed using the CellChat package. 31The possible interactions among cells were evaluated based on ligand-receptor pairs, leading to the construction of a ligand-receptor network.

| Analysis of the relationship between hub genes and clinical characteristics of PCa
The TCGA-PRAD dataset included 553 patients, with 10 deaths, five of which were specifically from PCa. Due to insufficient data to analyse the relationship between overall survival (OS), disease-free survival (DFS), and bone metastasis-specific death, our focus was on the relationship between hub genes and Progression Free Interval (PFI).Using the 'survival', 'survminer', and 'ggplot2' packages, we analysed the correlation between the seven hub genes and PFI in the TCGA-PRAD dataset.Additionally, we utilized the 'ggplot2', 'stats', and 'car' packages to investigate the association between APOC1 expression and the clinical characteristics of PCa, such as pathological stage, primary therapy outcome, residual tumour presence, Gleason score, and PFI.

| Cell viability assessment
The PCa cell line C4-2B was cultured in 96-well plates and transfected with corresponding si-RNAs.Cell viability was assessed using the Cell Counting Kit-8 (CCK-8) (Beyotime Biotechnology).After adding the CCK-8 reagent, the cells were incubated at 37°C for 1.5 h.
Viability was measured at 24, 48 and 72 h using an ELISA kit by reading the absorbance at 450 nm.

| Experiment on soft agar colony formation
1000 C4-2B cells/well and 500 PC-3 cells/well were seeded into 6-well plates, and cultured for 14 days to form clonal colonies.
These colonies were fixed with 4% paraformaldehyde, stained with 0.05% crystal violet for 1 h at room temperature, and then photographed.

| Transwell assays
Approximately 5 × 10 5 C4-2B cells transfected with the corresponding siRNA were seeded in the top chamber of a polycarbonate Transwell chamber and cultured at 37°C and with 5% CO 2 for 48 h.
The cells were fixed with 5% glutaraldehyde, stained with 0.05% crystal violet, and counted under a microscope.

| Cell scratch assay
C4-2B, PC3 cells transfected with si-RNA or si-NC were inoculated on a 6-well plate at a density of 1 × 10 5 cells per well.Use the tip of a 200 uL pipette to make scratches on the monolayer of cells.Wash the samples with PBS to remove cell debris.Next, the cells were cultured for 48 h, observed and photographed at 0 and 48 h.

| Statistical analysis
The R software was used to process and analyse the data.The p < 0.05 were considered statistically significant.

| Identification of DEGs1 and DEGs2
Differential expression analysis identified 7857 DEGs 1 between PRAD and normal groups in the TCGA-PRAD dataset, with 4039 upregulated and 3818 downregulated genes (Figure 2A; Figure S1A).In the GSE77930 datasets, we found 7166 candidate genes between PCa with bone metastasis and PCa, consisting of 4096 upregulated and 3070 downregulated genes, and 1163 candidate genes, with 521 upregulated and 651 downregulated genes in the GSE32269 datasets (Figure 2B,C; Figure S1B,C).
By intersecting candidate genes in the GSE77930 and GSE32269 datasets, we identified 186 DEGs 2 (Figure S1D,E).The analysis of DEGs 2 revealed enrichment in 168 GO terms, including 98 biological processes (BPs), 39 cellular components (CCs), and 31 molecular functions (MFs).The most prominent enrichment was found in terms related to extracellular matrix organization, extracellular structure organization, collagen-containing extracellular matrix, endoplasmic reticulum lumen, extracellular matrix structural constituent, and extracellular matrix binding (Figure 2D).

| Identification and functional analysis of DEGs3
We identified 625 DEGs3 between tumour and non-tumour cells in PCa with bone metastasis.These genes were then analysed using GO and KEGG enrichment analyses to understand their potential functions and associated pathways (Table S1).Results showed 903 GO terms and 38 KEGG pathways were enriched by DEGs3.For BP, terms related to leukocyte activation involved in immune response and myeloid leukocyte activation were predominant.For CC such as secretory granule lumen and vesicle lumen were significantly enriched.In MF, the most enriched terms included immune receptor activity, cell adhesion mediator activity, major histocompatibility complex (MHC) protein complex binding (Figure 2F).Key enriched KEGG pathways included leishmaniasis, haematopoietic cell lineage, tuberculosis, phagosome, inflammatory bowel disease (Figure 2G).

| Verification and expression analyses of hub genes
The AUC values of hub genes in the GSE77930 and GSE32269 datasets exceeded 0.7, indicating good diagnostic efficacy (Figure 3A,B).
Furthermore, the expression of these genes differed significantly between PCa with bone metastasis and PCa groups, exhibiting consistent trends.ADRB2, NR4A2 and ZNF331 had higher levels of expression in PCa than in PCa with bone metastasis groups, while the remaining 4 hub genes exhibited opposite trends (p < 0.05) (Figure 3C,D).In addition, immunohistochemical analysis further confirmed that APOC1, CFH, NR4A2 and NUSAP1 expression at the protein level was higher in primary PCa tissues than in PCa with bone metastasis tissues (p < 0.05) (Figure 3E,F,H), while NR4A2 exhibited the opposite trend (p < 0.05) (Figure 3G).These results were consistent with the results of the bioinformatics analysis.

| Common pathways and genes enriched by hub genes
Based genes.NR4A3 and NR4A1 were the genes exhibiting the highest correlations.The primary functions involved were epithelial cell proliferation and negative regulation of lipid biosynthetic processes (Figure 4D).

| PCa with bone metastasis exhibited a different immune microenvironment
The distribution of 28 immune cell types is illustrated in Figure 5A.
14 of these exhibited significant differences between PCa with and without bone metastasis, except for eosinophils, all others were more prevalent in PCa with bone metastasis than in PCa (Figure 5B).The correlation analysis revealed strong positive correlations between central memory CD4 T, gamma delta T and myeloid-derived suppressor cells with activated dendritic cells (p < 0.05, r > 0.8) (Figure 5C).As shown in Figure 5D

| Hub genes were performed single cell communication
After filtering out low-quality data (Figure S3), 2000 highly variable genes were selected for further analysis, with the top 15 genes highlighted in Figure S4A.The top 30 dimensions were used for tSNE and UMAP analyses (Figure S4B).Subsequently, 28 distinct cell subpopulations were annotated (Figure S4C).Of these, 11 types showed differential distribution: cells such as resting regulatory T (Treg) cells, Cytotoxic T lymphocytes (CTL)-1, CTL-2, and Mono2 were more prevalent in the normal group, while oteoblasts and tumour-associated macrophages (TAM) were more common in PCa with bone metastasis group (Figure S5).
Expression of hub genes across 28 cell subpopulations is shown in Figure S6.ADRB2 and APOC1 exhibited significant differences in 16 cell types between PCa with bone metastasis and PCa. LGALS1 and NUSAP1 differed in 11 cell types, CFH and NR4A2 in 10 and Cellular communication analysis was carried out to understand the interactions between PCa with bone metastasis and normal groups.Results indicated significant changes in interaction strength.
Communication between osteoblasts, pericytes, and endothelial cells was more frequent and stronger in PCa with bone metastasis (Figure S4D) than in the normal group (Figure S4E).

| Relationship between hub genes and clinical features
The available clinical information is summarized in Table 2.Among the seven hub genes, APOC1 had a significant correlation with PFI (p < 0.05) (Figure 6A).Furthermore, APOC1 expression was higher in stages T3 and T4 than in stage T2 (p < 0.01) (Figure 7A) and was greater in patients with lymph node matastasis (N1) compared to those without (N0) (p < 0.001) (Figure 7B).Initial treatment outcomes were categorized into complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD).APOC1 expression was significantly higher in the PR and PD groups than in the CR and SD groups (p < 0.001) (Figure 7D).It was also higher in the Gleason >7 group than in the ≤7 group (p < 0.001) (Figure 7C).In terms of residual tumour presence, APOC1 expression levels were significantly higher in the R1 and R2 groups than in the R0 group (p < 0.001) (Figure 7E).(Figure 8G).After 72 h, the si-APOC1 group exhibited significantly reduced C4-2B cell proliferation compared to the si-NC group (p < 0.05) (Figure 8H).

| DISCUSS ION
Bone metastasis significantly worsens the prognosis and survival of patients with PCa, yet its underlying mechanisms are not well understood.This study employed ML techniques to identify key genes associated with bone metastasis in PCa, potentially providing novel therapeutic targets for patient management.
First, we conducted a comprehensive analysis of bulk RNA-seq and scRNA-seq datasets from primary PCa and PCa with bone me- with AUC values ranging from 0.727 to 0.926.Furthermore, the expression of these markers was validated in pathological tissues obtained from patients with primary PCa and PCa with bone metastasis.Bioinformatics analysis revealed that APOC1, CFH, LGALS1 and NUSAP1 were highly expressed in bone metastases compared to primary tumours, whereas ADRB2, NR4A2 and ZNF331 exhibited the opposite trend (p < 0.05).Notably, these expression patterns were consistent with our predictions.
APOC1, encoding smallest apolipoprotein, has been linked to the progression of PCa.Silencing APOC1 halts cell cycle progression and enhances apoptosis in PCa cell lines; however, the underlying mechanism remains unclear. 33,34Complement factor H (CFH) serves as a marker of epithelial-mesenchymal transition (EMT) in PCa and is linked to cancer-associated fibroblasts (CAF). 35Nucleolar and spindle-associated protein 1 (NUSAP1) participates in the EMT of PCa, promoting its invasion and metastasis. 36The β2-adrenergic receptor (ADRB2) plays a role in the neuroendocrinisation of PCa (NEPC).Lower expression levels of ADRB2 are associated with shorter OS rates in patients with PCa. 37LGALS1 encodes galectin-1.
Silencing LGALS1 inhibits the growth and invasion of mCRPC cells through modulation of the androgen receptor (AR) and AKT pathway. 38NR4A2, a member of the nuclear receptor transcription factor superfamily, modulates the immune microenvironment of glioblastoma, enhancing, CD8 + T cell antigen presentation, microglial plasticity, and the therapeutic efficacy of immune checkpoint blockade in vivo. 39[42] Subsequent GSEA enrichment analysis of the seven pivotal genes identified KEGG pathways associated with these genes.This analysis revealed a potential association between these genes, except for CFH, and mitochondrial metabolic reprogramming, potentially contributing to the promotion of PCa-associated bone metastasis.Lasorsa et al. demonstrated an increase in adipogenesis and cholesterol production in PCa cells, influenced by AR signalling which also controls the mixed glycolysis and oxidative phosphorylation phenotype in these cells. 43In benign prostate tissue, mitochondrial energy metabolism is primarily based on the oxidative phosphorylation (OXPHOS) of substrates such as glutamate and malate.However, in PCa, OXPHOS mainly relies on succinate.
Furthermore, the severity of PCa positively correlates with the activity of the OXPHOS pathway. 44Thus, compared to hormonesensitive PCa, castration-resistant PCa exhibits heightened activity of the OXPHOS pathway, suggesting a regulatory role of OXPHOS in mitochondrial metabolism that may promote the progression of PCa.Given these findings, metabolic reprogramming has emerged as a promising target for treating PCa. 45 nalling pathway, promoting tumour angiogenesis, migration, and invasion. 9Given the role of monocytes in PCa cell invasiveness, 46 and the impact of dendritic cell infiltration on patient outcomes.The FDA approved sipuleucel-T for PCa treatment. 47Consequently, our findings suggest that the identified genes may serve as potential immunotherapy targets in PCa.
We conducted drug prediction and intercellular communication analysis, identifying aflatoxin B1 and benzo(a)pyrene as potent carcinogens involved in PCa development. 48,49The preventive and therapeutic effects of Cucumis sativus (Cucurbitaceae) seed oil and indole-3-carbinol (I3C) against PCa induced by these chemicals have been previously reported.Furthermore, cyclosporine may be a viable treatment for hormone-negative PCa and CRPC. 50 our intercellular communication analysis, we observed frequent crosstalk between osteoblasts, endothelial cells, and pericytes in PCa with bone metastasis.A close association between endothelial cells, pericytes, angiogenesis regulation, and tumour matrix remodelling within the tumour microenvironment of PCa has ben previously reported. 51Additionally, aging osteoblasts in osteopo- however, further research is required to elucidate the specific mechanisms involved.
In summary, we employed machine learning to comprehensively analyse bulk RNA-seq and scRNA-seq data from primary PCa and bone metastasis, identifying seven critical to bone metastasis.We preliminarily investigated their roles in this process, correlated them with clinical features, and validated them through histological and in vitro experiments.However, our study has some limitations: (1)   it used a small number of immunohistochemical samples from the same patient for both primary cancer and bone metastasis tissues; (2) the GEO database lacks comprehensive clinical and survival data for patients with bone metastases, while the TCGA database includes only a limited number of patients with metastasis (n = 4).
Therefore, conducting a more in-depth analysis of these genes is currently not feasible; (3) the mechanisms of these genes are not fully understood, necessitating further cell and animal experiments for verification.

| CON CLUS IONS
We employed ML to analyse scRNA-seq and bulk RNA-seq data, identifying seven hub genes associated with bone metastasis in PCa.
Their roles were further validated through immunohistochemistry, correlation with clinical characteristics of PCa, and in vitro experiments.Compared to traditional statistical methods, machine learning has the capability to handle more complex data patterns and provide more precise and comprehensive insights. 12In the future, Cadidate genes associated with bone metastasis PCa were identified by intersecting DEGs1, DEGs2 and DEGs3.Two machine-learning algorithms (Least Absolute Shrinkage and Selection Operator [LASSO] and Support Vector Machine-Recursive Feature Elimination [SVM-RFE]) on GSEA, 47, 50, 57, 27, 22, 36 and 23 KEGG pathways were enriched by APOC1, LGALS1, CFH, NUSAP1, NR4A2, ADRB2 and ZNF331 respectively.Top 5 significantly enriched pathways for each hub genes are shown in Figure 4A-C; Figure S2A-D.Oxidative phosphorylation was enriched by APOC1, LGALS1, NUSAP1, NR4A2, ADRB2 and ZNF331.Parkinson's and Huntington's disease were both enriched by APOC1, NUSAP1, NR4A2, ADRB2 and ZNF331.Additionally, a gene interaction network was constructed around the hub genes with 20 neighbouring , CFH, APOC1 and LGALS1 were positively correlated with the most differential immune cells (p < 0.05, r > 0.5).Conversely, NR4A2, NUSAP1, ADRB2 and ZNF331 were negatively correlated with differential immune cells such as CD56dim natural killer cells, monocytes and plasmacytoid dendritic cells (p < 0.05, r < −0.5).Moreover, drug F I G U R E 2 Differential gene expression analysis, enrichment analysis and identification of potential genes associated with bone metastasis in PCa.(A-C) Volcano plot illustrating the differential expression of genes.(D) DEGs2 GO enrichment analysis.(E) DEGs2 KEGG enrichment analysis.(F) DEGs3 GO enrichment analysis.(G) DEGs3 KEGG enrichment analysis.(H, I) LASSO regression of 11 genes.(J) SVM-RFE analysis.(K) DEGs 1,2,3 Venn diagram illustrating candidate genes.(L) Intersection of two machine learning feature genes.prediction for the hub genes identified aflatoxin B1, benzo(a)pyrene and cyclosporine as drugs targeting all seven hub genes simultaneously (Figure 5E,F).

F I G U R E 3
Critical genetic diagnostic efficacy and Revalidation of genes expression.(A) ROC Curve Analysis of Key Genes in GSE77930.(B) ROC Curve Analysis of Key Genes in GSE32269.(C) Verification of key genes expression in GSE77930.(D) Verification of key genes expression in GSE77930 in GSE32269.(E) Differential expression of APOC1 protein in primary PCa tissues, bone metastasis tissues and representative images of immunohistochemistry. (F) Differential expression of CFH protein in primary PCa tissues, bone metastasis tissues and representative images of immunohistochemistry. (G) Differential expression of NR4A2 protein in primary PCa tissues, bone metastasis tissues and representative images of immunohistochemistry. (H) Differential expression of NUSAP1 protein in primary PCa tissues, bone metastasis tissues and representative images of immunohistochemistry.

Furthermore, the
levels of expression of APOC1 were elevated in patients with disease progression compared to those without (p < 0.001) (Figure7F).Logistic regression analysis identified that pathological stages T3/4, lymph node metastasis (PR/PD), residual tumours (R1/R2), and a Gleason score >7 as risk factors for high APOC1 expression in PCa (Figure6H).

F I G U R E 4 F I G U R E 5
GSEA enrichment analysis of hub genes and reconstruction of co-expression networks.(A-C) GSEA analysis of APOC1, LGALS1 and NR4A2.(D) Co-expression network construction.Analysis of tumour microenvironment and drug predictors.(A) Heat map of immune cells infiltration.(B) Box plot illustrating variations in immune cells infiltration.(C) Diagram illustrating the correlation between different immune cell types.(D) Correlation between key genes and differential immune cells.(E) Venn diagram of key genes drug predictors.(F) Network diagram for predicting key genes and drug interactions.

3. 9 |
The effect of APOC1 on the proliferation of PCa cellsTo investigate the role of APOC1 in PCa cells, we conducted in vitro experiments using C4-2B and PC3 cell lines.RT-PCR demonstrated a significant down-regulation of APOC1 expression in both C4-2B and PC3 cell lines following si-APOC1 transfection (p < 0.001) (Figure8A,B).APOC1 silencing significantly impaired the colony formation and migration ability of both C4-2B and PC3 cells (Figure8C-F).Similarly, transwell assays revealed that silencing APOC1 silencing decreased the migration and invasion ability of C4-2B cells (p < 0.05) tastasis.Utilizing LASSO and SVM-REF machine learning methods, we identified seven molecular markers (ADRB2, APOC1, CFH, LGALS1, NR4A2, NUSAP1 and ZNF331) for predicting bone metastasis.Both training and validation sets exhibited high diagnostic performance, Our research identified a higher enrichment of APOC1, LGALS1, NUSAP1, NR4A2, ADRB2 and ZNF331 in the OXPHOS pathway, in PCa with bone metastasis, suggesting that targeting these genes could potentially reverse aberrant metabolic pathways and improve treatment outcomes.Further investigation is warranted understand their underlying mechanisms.Next, we analysed immune cell infiltration in patients with PCa and with or without bone metastasis, finding a positive correlation between CFH, APOC1 and LGALS1 and various immune cell types.Conversely, NR4A2, NUSAP1, ADRB2 and ZNF331 negatively correlated with immune cells such as natural killer cells, monocytes and plasmacytoid dendritic cells.The potential mechanism by which CX3CR1 influences CD14 + CD16 − monocytes to facilitate the progression of PCa is through activation of the CX3CR1/CX3CL1 sig- rosis have been shown to promote vascular endothelial cell senescence and apoptosis through exosome-mediated signalling. 52 I G U R E 6 Correlation analysis between APOC1 and clinical features based on TCGA database.(A) The predictive value of APOC1 for PFI in prostate cancer.(B) The predictive value of ADRB2 for PFI in prostate cancer.(C) The predictive value of CFH for PFI in prostate cancer.(D) The predictive value of LGALS1 for PFI in prostate cancer.(E) The predictive value of NR4A2 for PFI in prostate cancer.(F) The predictive value of NUSAP1 for PFI in prostate cancer.(G) The predictive value of ZNF331 for PFI in prostate cancer.(H) The forest plot was utilized to demonstrate the risk factors associated with high expression of APOC1 mRNA, as determined through logistic regression analysis.However, the precise mechanisms underlying the extensive cellular communication among osteoblasts, endothelial cells, and pericytes in bone metastasis of PCa are not fully understood, necessitating further research to clarify their roles.Next, we investigated the association between hub genes and clinical characteristics of PCa using clinical data on PFI in PCa from the TCGA database, due to the lack of other specific data for patients with bone metastasis.Our analysis revealed a significant correlation between APOC1 expression levels and various clinical features associated with PCa progression, including PFI, pathological stage, primary treatment outcome, tumour residue, Gleason score, and lymph node metastasis, indirectly suggesting a close relationship between APOC1 and PCa metastasis.Furthermore, our in vitro study demonstrated that silencing APOC1 significantly reduced proliferation, clonality, migration and invasion of PCa cells, indicating that APOC1 could be a therapeutic target for PCa with bone metastasis;

F I G U R E 7 54 F I G U R E 8
Correlation analysis between APOC1 and clinical features based on the TCGA database was conducted to investigate the association between mRNA expression of APOC1 and various clinical characteristics, including: (A) Pathologic T stage.(B) Pathologic N stage.(C) Gleason score.(D) Primary therapy outcome.(E) Residual tumour.(F) PFI event.*p < 0.05, **p < 0.01 and ***p < 0.001.multidimensional data related to PCa with bone metastasis, including genomics, epigenetics, transcriptomics, proteomics, metabolomics, can be integrated through ML, which will help to gain a deeper understanding of tumour biology and unravelling tumour plasticity and heterogeneity. 53,Effects of silencing APOC1 were detected in vitro.(A, B) RT-PCR was used to detect the efficiency of APOC1 down-regulation in C4-2B and PC-3 cell lines.(C, D) Colony formation assay was used to detect the cell viability of C4-2B and PC-3 cell lines.(E, F) Cell scratch assay was used to detect the migration ability of C4-2B and PC-3 cell lines.(G) Transwell assay was used to detect the migration ability of C4-2B cell line.(H) CCK-8 assay was used to detect the proliferation ability of C4-2B cell line.*p < 0.05, **p < 0.01, ***p < 0.001 and ****p < 0.0001.

Table 1 .
Genome U133A Array) comprised 51 samples, including 22 PCa and 29 PCa with bone metastasis samples.The GSE143791 dataset included 9 PCa with bone metastases and 7 normal samples.

TA B L E 1 Details of the cases used in this study.
Associations between APOC1 expression and clinical features of patients with PCa in TCGA database.
TA B L E 2